CT-AI · Question #101
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as…
The correct answer is B. By performing optimization of the data from past iterations to see where the most common. AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and…
Question
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle. How can an AI-based tool be expected to assist in this reduction?
Options
- ABy using a clustering method to quantify the relationships between test cases and then assigning
- BBy performing optimization of the data from past iterations to see where the most common
- CBy performing bayesian analysis to estimate the types of human interactions that are expected to
- DBy using A/B testing to compare the last update with the newest change and compare metrics
How the community answered
(42 responses)- A2% (1)
- B74% (31)
- C7% (3)
- D17% (7)
Explanation
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
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